bulk-rnaseq vs Frontend Design
Сравнение двух Agent Skills бок о бок. Выбери подходящий для Claude Code или Claude Desktop.
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, fe
Design clean, modern UI components in code
Сравнение
| Параметр | bulk-rnaseq | Frontend Design |
|---|---|---|
| Скрипты | есть | нет |
| Разрешённые инструменты | любые | 1 |
| Тип установки | personal | project |
| Категория | design | design |
| Проверен | — | |
| Лицензия | MIT | MIT |
| Автор | K-Dense AI | Anthropic |
| Repo | K-Dense-AI/scientific-agent-skills | anthropics/skills |
Когда брать bulk-rnaseq
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
Когда брать Frontend Design
Guides Claude to produce well-structured, accessible and visually balanced frontend components with sensible spacing, typography and responsive behavior.
Ищешь другое? Все скиллы.